The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy
Abstract
Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.
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Comparative evaluation of eight docking tools for docking and virtual screening accuracy
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Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762
Molecular dynamics simulations and novel drug discovery
Liu, X., Shi, D., Zhou, S., Liu, H., Liu, H., & Yao, X. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1403419 · Cited by 518
FRED and HYBRID docking performance on standardized datasets
McGann, M. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9584-8 · Cited by 503
Boosting docking-based virtual screening with deep learning.
Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395
Docking Challenge: Protein Sampling and Molecular Docking Performance
Elokely, K. M., & Doerksen, R. J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400040d · Cited by 287
An artificial intelligence accelerated virtual screening platform for drug discovery
(2024) · doi:10.1038/s41467-024-52061-7 · Cited by 259
Cang, Z., Mu, L., & Wei, G.-W. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005929 · Cited by 257
Beware of machine learning-based scoring functions-on the danger of developing black boxes.
Gabel, J., Desaphy, J., & Rognan, D. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500406k · Cited by 143
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., & Carlson, H. A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400025f · Cited by 133
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107
The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening.
Kalliokoski, T., Salo, H. S., Lahtela-Kakkonen, M., & Poso, A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900364w · Cited by 66
Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations
Karrenbrock, M., Rizzi, V., Procacci, P., & Gervasio, F. L. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c06516 · Cited by 5
Metadata
- Authors
- Cross, J. B., Thompson, D. C., Rai, B. K., Baber, J. C., Fan, K. Y., Hu, Y., & Humblet, C.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-05-28
- Volume
- 49
- Issue
- 6
- Pages
- 1455-1474
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2093426296
- Cited by
- 446
- References
- 5
- FWCI
- 30.58
- Type
- journal article
- Language
- en
- Open access
- closed
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